Enhancement techniques for blind source separation (bss)

ABSTRACT

This disclosure describes signal processing techniques that can improve the performance of blind source separation (BSS) techniques. In particular, the described techniques propose pre-processing steps that can help to de-correlate the different signals from one another prior to execution of the BSS techniques. In addition, the described techniques also propose optional post-processing steps that can further de-correlate the different signals following execution of the BSS techniques. The techniques may be particularly useful for improving BSS performance with highly correlated audio signals, e.g., from two microphones that are in close spatial proximity to one another.

This application claims the benefit of U.S. Provisional Application No.60/797,264, filed May 2, 2006, the entire content of which isincorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to signal processing and, more particularly,processing techniques used in conjunction with blind source separation(BSS) techniques.

BACKGROUND

In many signal processing applications, different signals are oftencorrupted with noise. This noise may include such things as backgroundsounds, disturbances, interference, cross-talk, or any unwanted additionto a recorded signal. Accordingly, in order to enhance the signals, itis desirable to reduce or eliminate this noise. In speech communicationprocessing, signal processing for noise reduction is often called speechenhancement.

Blind source separation (BSS) can be used to restore independent sourcesignals using multiple independent signal mixtures of the sourcesignals. In order to separate two signals, two or more sensors areneeded to generate independent signal mixtures. Each sensor is placed ata different location, and each sensor records a signal, which is amixture of the source signals. The recorded signals are independent fromone another, however, because the sensors record the information atdifferent locations. BSS algorithms may be used to separate signals byexploiting these signal differences, which manifest the spatialdiversity of the common information that was recorded by both sensors.In speech communication processing, the different sensors may comprisemicrophones that are placed at different locations relative to thesource of the speech that is being recorded.

SUMMARY

This disclosure describes signal processing techniques that can improvethe performance of blind source separation (BSS) techniques. Inparticular, the described techniques include pre-processing steps thatcan help to de-correlate independent sensor signals from one anotherprior to execution of the BSS techniques. In addition, the describedtechniques also may include optional post-processing steps that canfurther de-correlate the different signals following execution of theBSS techniques. The techniques may be particularly useful for improvingBSS performance with highly correlated audio signals, e.g., audiosignals recorded by two microphones that are in close spatial proximityto one another.

In one embodiment, this disclosure describes a method comprisingreceiving a first signal associated with a first sensor and a secondsignal associated with a second sensor, pre-processing the second signalto de-correlate the second signal from the first signal, applying a BSStechnique to the first signal to generate a first BSS signal, andapplying the BSS technique to the pre-processed second signal togenerate a second BSS signal.

In another embodiment, this disclosure describes a device comprising afirst sensor that generates a first signal and a second sensor thatgenerates a second signal, a pre-processing unit that pre-processes thesecond signal to de-correlate the second signal from the first signal,and a BSS unit that applies a BSS technique to the first signal and thepre-processed second signal to generate first and second BSS signalsrespectively. Optionally, the device may also include a post-processingunit to further de-correlate the BSS signals.

In another embodiment, this disclosure describes an apparatus comprisingmeans for generating a first signal, means for generating a secondsignal, means for pre-processing the second signal to de-correlate thesecond signal from the first signal, and means for applying a BSStechnique to the first signal and the pre-processed second signal togenerate first and second BSS signals respectively. Optionally, meansfor post-processing the BSS signals may also be applied to one or bothof the BSS signals to further de-correlate the BSS signals.

These and other techniques described in this disclosure may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the software may be executed in a digitalsignal processor (DSP) or other type of processor. The software thatexecutes the techniques may be initially stored in a machine-readablemedium and loaded and executed in the processor for effectiveenhancement of BSS techniques.

Accordingly, this disclosure also contemplates a machine-readable mediumcomprising instructions that upon execution receive a first signal and asecond signal, pre-process the second signal to de-correlate the secondsignal from the first signal, and apply a BSS technique to the firstsignal and the pre-processed second signal to generate first and secondBSS signals respectively.

Additional details of various embodiments are set forth in theaccompanying drawings and the description below. Other features, objectsand advantages will become apparent from the description and drawings,and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a device that performs a blind sourceseparation (BSS) technique that may be enhanced by the pre-processingand post-processing techniques described herein.

FIG. 2 is a block diagram illustrating a device that performspre-processing and post-processing techniques relative to an otherwiseregular BSS technique according to an embodiment of this disclosure.

FIG. 3 is another block diagram illustrating a device that performspre-processing and post-processing techniques relative to a BSStechnique according to an embodiment of this disclosure.

FIG. 4 is a flow diagram illustrating a technique that may be performedaccording to an embodiment of this disclosure.

DETAILED DESCRIPTION

This disclosure describes signal processing techniques that can improvethe performance of blind source separation (BSS) techniques. Inparticular, the described techniques include pre-processing steps thatcan help to de-correlate independent sensor signals from one anotherprior to execution of the otherwise regular BSS techniques. In addition,the described techniques may also include optional post-processing stepsthat can further de-correlate the separated signals following executionof the BSS techniques. The techniques described herein may beparticularly useful for improving the performance of BSS algorithmsperformance with highly correlated audio signals, e.g., audio signalsrecorded by two microphones that are in close spatial proximity to oneanother.

Some BSS algorithms may have limited effectiveness, particularly whentwo sensors are positioned very close to one another. For handhelddevices that perform speech enhancement, for example, it may bedesirable to have a microphone arrangement in which the differentmicrophones are positioned in close spatial proximity to one another.Indeed, handheld devices, including wireless communication devices suchas mobile telephones, are commonly designed with small form factors inorder to promote user convenience, which presents challenges for BSS dueto the close spatial proximity of different microphones.

In general, BSS is used to separate independent signals using multiplemixtures of these signals. In the techniques described in thisdisclosure, improvements are described for use with otherwise regularBSS algorithms. The described techniques may use an adaptive filter tode-correlate recorded signals as part of a pre-processing procedure.Then, the de-correlated signals can be calibrated as part of thepre-processing. After calibration, a BSS feedback architecture based oninformation maximization may be used to separate the de-correlatedsignals. Optionally, a second adaptive filter may be used as part of apost-processing procedure to further improve signal separationperformance by further de-correlating the signals.

FIG. 1 is a block diagram of a device 10 that performs a BSS techniquethat may be enhanced by the pre-processing and post-processingtechniques described in this disclosure. BSS is also known asindependent component analysis (ICA), and can be used to separatesignals based on multiple mixtures of these signals. During theseparation process, a number of recorded sensor signals, which aremixtures of source signals, are available. Typically, informationregarding the mixing process is not available, and no directmeasurements of source signals is available. Sometimes, a prioristatistical information of some or all source signals may be available.

BSS has attracted broad attention from researchers due to its potentialvalue in many signal processing problems. BSS has potential applicationsin various areas, such as communications, speech enhancement, noisereduction, and bio-medical signal processing including electrocardiogram(ECG) and electroencephalogram (EEG) processing, and the like. Thetechniques described herein may be particularly useful for speechapplications in wireless communication devices where small form factorslimit microphone placement. However, the techniques are not limited tospeech applications and may be useful in other signal processingapplications, or other devices.

Among a number of BSS approaches, an information maximization basedmethod is of much interest because of its simplicity and its suitabilityfor real-time implementation on fixed-point platforms. Such BSSapproaches may be used for separating convolutive signal mixtures. Inparticular, feedback cross-filters can be used to separate convolutivelymixed signals.

As shown in FIG. 1, device 10 includes a sensor unit 12 and a BSS unit14. The variables S₁(t) and S₂(t) represent two independent sourcesignals. Filter h₁₁(t), h₁₂(t), h₂₁(t), h₂₂(t) represent convolutivepaths between the sensors and the sources. Sensors 16A and 16B areillustrated as adders, but more generally represent two differentsensors, such as microphones, located at different locations. If used inspeech enhancement, it may be desirable for sensors 16A and 16B tocomprise omni-directional microphones, as these are cost effective.

Signal S₁(t) convolves with path h₁₁(t) before it reaches sensor 16A andconvolves with path h₁₂(t) before it reaches sensor 16B. Similarly,signal S₂(t) convolves with path h₂₁(t) before it reaches sensor 16A andconvolves with path h₂₂(t) before it reaches sensor 16B. Sensors 16A and16B capture the respective information to generate signals x₁(t) andx₂(t), respectively. Then, ADCs 17A and 17B generate signals X₁(z) andX₂(z), respectively. Thus, signals X₁(z) and X₂(z) are in digital domainafter analog-to-digital conversion. These signals X₁(z) and X₂(z) arefed to BSS unit 14 in order to perform the blind source separation.

In particular, BSS unit 14 implements a feedback cross filteringtechnique. X₁(z) is fed into a filter having transfer function W₁₁(z)and then to adder 18A. X₂(z) is similarly fed into a filter havingtransfer function W₂₂(z) and then to adder 18B. The output of adder 18Ais fed into another filter having transfer function W₁₂(z) before beingfed back to adder 18B. The output of adder 18B is fed into anotherfilter having transfer function W₂₁(z) before being fed back to adder18B. In FIG. 1, the variables S′₁(z) and S′₂(z) represent the outputs ofBSS unit 14 which typically resemble source signal S₁(t) and S₂(t) andhave much better separation than input signals X₁(z) and X₂(z) to BSSunit 14.

The filters represented by transfer functions W₁₂ (z) and W₂₁(z) can beimplemented using least mean square (LMS) like adaptive filteringalgorithm. Additional details of such filtering is discussed in greaterdetail below in the discussion of FIG. 2. If desired, non-lineartransfer functions denoted as Ψ(•) (not shown in FIG. 1) can be usedduring filter coefficient update. Possible forms of the function Ψ(•)include Ψ(x)=tanh(x), Ψ(x)=|x|, and Ψ(x)=2P_(X)(x)−1, where P_(X)(x) isthe cumulative distribution function (cdf) of random variable X. Thefunctions denoted as Ψ(•) are not shown in FIG. 1, however, forsimplicity.

The transfer functions W₁₁(z) and W₂₂(z) may take several formsconsistent with BSS techniques. However, for added simplicity, in thefollowing discussion, W₁₁(z) and W₂₂(z) can be replaced by scalarswithout compromising the performance of the algorithm. Accordingly, inthe following discussion, these two transfer functions are set to beunity and removed from subsequent figures.

A BSS algorithm like that implemented by BSS unit 14 is very simple toimplement and yields fairly good separation performance in many cases.However, such an algorithm may have difficulty in converging whenrecordings are highly correlated. When this happens, the BSS algorithmmay tend to annihilate the most prominent component in all signals. Inspeech enhancement, the most prominent component is most likely thedesired speech signal. For this reason, this disclosure implements apre-processing unit to address these potential problems.

In some cases, the BSS algorithm like that implemented by BSS unit 14does not fully exploit available information to separate the signals. Inthis case, correlation between the separated signals may still beobserved following separation by BSS unit 14. This leaves room forfurther improvement on the performance of the algorithm. For thisreason, this disclosure may also implement a post-processing unit isprovide further de-correlation. The improved BSS techniques describedbelow may be especially useful for applications where only one sourcesignal is interested, like in the case of multi-microphone noisereduction in speech communication.

Generally, in BSS algorithms, all signals are treated as independentrandom variables. The assumption used to blindly separate signals isthat all random variables are statistically independent to each other,i.e., the joint distribution of all random variables is the product ofall individual random variables. This assumption can be formulated as:

P _(S) ₁ _(, . . . S) _(m) (s ₁ , . . . s _(m))=P _(S) ₁ (s ₁) . . . P_(S) _(m) (s _(m)),

where P_(S) ₁ _(, . . . S) _(m) (s₁, . . . s_(m)) is joint distributionof all random variables S₁, . . . , S_(m) and P_(S) _(j) (s_(j)) is thedistribution of the jth random variable S_(j).

The BSS problem may be called an instantaneous BSS problem if the mixingprocess can be modeled as instantaneous matrix mixing, which isformulated as:

x (t)=A s (t),

where s(t) is an m×1 vector, x(t) is an n×1 vector, A is an n×m scalarmatrix. In the separation process, an m×n scalar matrix B is calculatedand used to reconstruct signal ŝ(t)=B x(t)=BA s(t) such that ŝ(t)resembles s(t) up to arbitrary permutation and arbitrary scaling. Inother words, matrix BA can be decomposed to PD, where matrix P is apermutation matrix and matrix D is a diagonal matrix. A permutationmatrix is a matrix derived by permuting an identity matrix of the samedimension. A diagonal matrix is a matrix that only has non-zero entrieson its diagonal. Note that the diagonal matrix D does not have to be anidentity matrix. If all m sources are independent from one another,there should not be zero entry on diagonal of matrix D. In general, n≧mis required for complete signal separation.

Unfortunately, in reality, few problems can be modeled usinginstantaneous mixing. Signals typically travel through non-idealchannels before being recorded by sensors, as illustrated in FIG. 1. Inthis case, the problem is considered as convolutive BSS problem. Itsmixing process can be modeled as:

${{x_{i}(t)} = {{\sum\limits_{j = 1}^{m}{{{h_{ij}(t)} \otimes {s_{j}(t)}}\mspace{31mu} i}} = 1}},\ldots \mspace{11mu},{n.}$

Here, s_(j)(t) is the jth source and x_(i)(t) is the measurement by theith sensor. Transfer function h_(ij)(t) is the transfer function betweenthe jth source and the ith sensor. Symbol {circle around (×)} denotesconvolution. Another set of filters W_(ji)(z) is needed to restoresource signals s(t).

The restoration formula is:

${{S_{j}^{\prime}(z)} = {{\sum\limits_{i = 1}^{n}{{W_{ji}(z)}{X_{i}(z)}\mspace{31mu} j}} = 1}},\ldots \mspace{11mu},{m.}$

Here, Z-domain representation is used because the separation process isexecuted in digital signal domain. Similar to the instantaneous mixingproblem, S′ (z) resembles S(z), which is a discrete representation ofsource signals s(t), up to an arbitrary permutation and an arbitraryconvolution. If mixing transfer functions h_(ij)(t) are expressed usingdiscrete representation H_(ij)(t), the overall system can be formulatedas:

W(z)H(z) = PD(z), where ${{H(z)} = \begin{bmatrix}{H_{11}(z)} & {H_{21}(z)} \\{H_{12}(z)} & {H_{22}(z)}\end{bmatrix}},{{W(z)} = {{\frac{1}{1 - {{W_{12}(z)}{W_{21}(z)}}}\begin{bmatrix}1 & {- {W_{21}(z)}} \\{- {W_{12}(z)}} & 1\end{bmatrix}}.}}$

P is a permutation matrix and D(z) is a diagonal transfer functionmatrix. The elements on the diagonal of D(z) are transfer functionsrather than scalars as represented in the instantaneous BSS problems.Meanwhile, the requirement for complete separation in instantaneous BSS,m≧n, still holds in convolutive BSS.

In accordance with this disclosure, it may be assumed that two sensorsare used. Of all source signals, only one signal is consideredinteresting and needs to be enhanced. Nevertheless, although a focus isgiven to two-sensor configurations, the result can be easily extended tomultiple-sensor configurations and multiple interested signals.

Referring again to FIG. 1, the output of the algorithm implemented byBSS unit 14 can be formulated as:

S′ ₁(z)=W ₁₁(z)X ₁(z)+W ₂₁(z)S′ ₂(z)

S′ ₂(z)=W ₂₂(z)X ₂(z)+W ₁₂(z)S′ ₁(z).

As noted above, because W₁₁(z) and W₂₂(z) do not contribute to theperformance of the algorithm, they can be set to be scalars, like unity.In the following, W₁₁(z) and W₂₂(z) shown in FIG. 1 are set to be unityfor later figures and will not appear although they could be added. Inthis case, the output of this algorithm implemented by BSS unit 14 canbe formulated as:

S′ ₁(z)=X ₁(z)+W ₂₁(z)S′ ₂(z)

S′ ₂(z)=X ₂(z)+W ₁₂(z)S′ ₁(z).

In some cases, W₁₂(z) and W₂₁, (z) can be adapted using least meansquare (LMS) like adaptive filtering algorithm. A possible filter updateequation is given below.

w ₁₂(t)= w ₁₂(t−1)−2μφ(s′ ₂(t)) s′ ₁(t)

w ₂₁(t)= w ₂₁(t−1)−2μφ(s′ ₁(t)) s′ ₂(t)′

where w ₁₂(t) and w ₂₁(t) are the two filters at time t. The variabless′₁(t) and s′₂(t) are filtered output at time t. They are also discreterepresentation of S′₁(t) and S′₂(t), respectively. The variable μ isupdate step size, and φ(•) is a non-linear function (not shown in FIG.1). By way of example, the transfer function represented by variableφ(•) can be sigmoid function, a hyperbolic tangent function or a signfunction. It can also be another monotonous function evaluated between−1 and 1. s′₁(t) and s′₂(t) are input to filter w ₁₂(t) and w ₂₁(t) attime t, respectively, i.e.,

s′ ₁(t)=[s′ ₁(t−1)s′ ₁(t−1) . . . s′ ₁(t−M)]^(T)

s′ ₂(t)=[s′ ₂(t−1)s′ ₂(t−1) . . . s′ ₂(t−M)]^(T′)

where M is length of the two filters.

FIG. 2 is a block diagram illustrating a device 20 that performspre-processing and post-processing techniques relative to an otherwiseregular BSS technique according to an embodiment of this disclosure. Inparticular, device 20 includes pre-processing unit 22, a BSS unit 24 anda post-processing unit 26. BSS unit 24 is simplified relative to BSSunit 14 in FIG. 1, but may comprise additional components consistentwith any BSS technique. In any case, BSS unit 24 generally comprisescomponents that perform an otherwise regular BSS algorithm.Pre-processing unit 22, however, modifies the BSS techniques bypre-processing the signals to improve de-correlation prior to executionof a regular BSS technique. In addition, post-processing unit 26 furthermodifies the algorithm to achieve additional de-correlation of thesignals following the regular BSS technique. Post-processing unit 26 isoptional in some embodiments.

In FIG. 2, signals X₁(z) and X₂(z) are signals from two differentsensors, e.g., audio signals from two different microphones, such as twoomni-directional microphones. First signal X₁(z) is sent to adder 32A ofBSS unit 24, and is also sent to pre-processing unit 22. Inpre-processing unit 22, adaptive filter 42 is used to filter X₁(z) andthe output of adaptive filter 42 is subtracted from X₂(z) viasubtraction unit 44 to generate X₂′(z). Adaptive filter 42 (“G(z)”) mayinclude a relatively small number of taps, e.g., three taps. X₂′(z)comprises mostly noise insofar as it essentially represents thedifference between X₁(z) and X₂(z) following filtering of X₁(z). X₂′(z)is also used as feedback to control adaptive filter 42.

Calibration unit 46 uses X₂′(z) and X₁(z) to generate a calibrationfactor “c.” Specifically, calibration unit 46 determines the noise floorin X₁(z) by dividing X₁(z) into time segments and monitoring energy overa period. The minimum energy over this period is established as thenoise floor of X₁(z). Calibration unit 46 similarly determines the noisefloor in X₂′(z), e.g., by dividing X₂′(z) into segments, monitoringenergy over a period, and identifying a minimum energy value of X₂′(z)as the noise floor. A ratio of the noise floor of X₁(z) relative to thatof X₂′(z) establishes calibration factor “c,” which can be used to scaleX₂′(z) so that X₂″(z) has a comparable noise floor to X₁(z).Multiplication unit 48 applies calibration factor “c” to X₂′(z) togenerate X₂″(z) which is referred to herein as the pre-processed versionof second signal X₂(z).

Pre-possessed second signal X₂″(z) is more de-correlated from X₁(z) thanthe original first signal X₂(z). For this reason, pre-processing unit 22can improve performance of blind source separation that is performed byBSS unit 24. BSS unit 24 generally operates similar to BSS unit 14 ofFIG. 1, but operates using a pre-processed second signal X₂″(z) ratherthan signal X₂(z). Filter W₁₂(z) applies least mean square (LMS)adaptive filtering to first signal X₁(z), and adder 32B sums the LMSadaptive filtered first signal with the pre-processed second signalX₂″(z). Similarly, filter W₂₁(z) applies LMS adaptive filtering topre-processed second signal X₂″(z), and adder 2A sums the LMS adaptivefiltered and pre-processed second signal with the first signal X₁(z).

As noted above, adaptive filter 42 does not require many taps. For manyapplications, one to three taps are sufficient. If three taps are used,it may be desirable to dedicate one non-causal tap to address differentsensor configurations, i.e. adding one sample delay to signal X₂(z).Notably, more taps may actually degrade the performance of theconvolutive BSS algorithm by unnecessarily removing spatial diversitybetween signals. Therefore, three or fewer taps may be preferred forperformance reasons.

The output of adaptive filter 42 can be denoted as

${x_{2}^{\prime}(t)} = {{x_{2}\left( {t - \tau} \right)} - {\sum\limits_{i = 1}^{N}{{g_{l}(i)}{{x_{1}\left( {t - i} \right)}.}}}}$

where τ is the delay added to signal X₂(z), g_(t)(i) is the ith filtercoefficient at time t, and N is length of the adaptive filter. Adaptivefilter 42 can be updated using following equation:

g _(t+1) = g _(t)+2μx ₂′(t) x ₁(t),

where g _(t)=[g_(t)(1) g_(t)(2) . . . g_(t)(N)]^(T) represents thefilter coefficient of adaptive filter 42 at time t, x₂′(t) is filteroutput at time t, μ is the adaptation step size, and x ₁(t)=[x₁(t)x₁(t−1) . . . x₁(t−N+1)]^(T) is input to filter G(z).

In many cases, the output level of adaptive filter 42 is very lowbecause the difference between signals X₁(z) and X₂(Z) is very small. Ifthis signal is fed to BSS algorithm directly, the algorithm tends toconverge very slowly due to unbalanced excitation on two channels.Therefore, it is desirable to calibrate this signal up to certain levelto speed up BSS algorithm's convergence as described herein. Inparticular, the error signal can be calibrated up so that the noiselevel in the error signal is similar to that of a primary microphonesignal. To be specific, if L₁ denotes the noise floor level in X₁(z) andL₂ denotes the noise floor level in X′(z), the calibration factor c canbe chosen to be

c=L ₁ /L ₂.

The outputs of BSS unit 24 are referred to herein as BSS signals. Insome cases, these may comprises the final processed signals having beende-correlated from one another, in which case post-processing unit 26can be eliminated. However, performance of the BSS algorithm may befurther improved by implementing a post-processing unit 26.

Generally, after the signal separation process, two signals aregenerated, which again are referred to as BSS signals. The first BSSsignal, which is the output of adder 32A contains primarily theinterested signal and somewhat attenuated portions of all other signals.For speech enhancement applications, this first BSS signal may comprisethe recorded speech with attenuated noise. The reduction of noise in thefirst BSS signal may vary depending on environment and properties of thespeech signal and the noise. The second BSS signal, which is the outputof adder 32B, contains primarily the noise, with the interested signal(such as speech detected by two different microphones) having beenattenuated.

In many cases, there is still correlation between the first and secondBSS signals. This correlation may be further exploited to improve thesignal of interest, e.g., the speech signal. For this purpose, apost-processing unit 26 can be used to further de-correlate the firstand second BSS signals.

In particular, as shown in FIG. 2, post-processing unit 26 includes anadaptive filter 52 to filters the second BSS signal, which is outputfrom adder 32B. First BSS signal, which is output from adders 32A, isdelayed via delay circuit 54. Then, the adaptively filtered second BSSsignal (output from adaptive filter 52) is subtracted from the delayedfirst BSS signal (output from delay circuit 54) via subtraction unit 56.S₁′(z) represents the output associated with input signal X₁(z)following the pre-processing, BSS techniques and post-processing.Similarly, S₂′(z) represents the output associated with input signalX₂(z) following the pre-processing, BSS techniques and post-processing.The S₁′(z) signal is also used as feedback to control adaptive filter52.

In device 20 of FIG. 2, only one of the two signals may be desired,i.e., signal S₁′(z), which may represent input signal X₁(z) withunwanted noise removed. Here, the other output signal S₂′(z) is simplyused as input to adaptive filter 52. As noted above, following delaycircuit 54, the interested signal S₁′(z) is used as reference toadaptive filter 52. The adaptation of adaptive filter 52 may be similarto that of adaptive filter 42 in pre-processing unit 22. Post-processingunit 26 may be particularly useful for speech enhancement where twodifferent microphones are used to generate one enhanced speech signal,which are typically omni-directional microphones. In this case, theenhanced speech signal may be output as S₁′(z).

FIG. 3 illustrates another embodiment of a device 60 according to anembodiment of this disclosure. Device 60 of FIG. 3 is very similar todevice 20 of FIG. 2. However, in device 60, both signals (S₁″(z) andS₂″z)) are of interest, not just signal S₁′(z) as discussed above withreference to FIG. 2. Therefore, in FIG. 3, a different post processingunit 70 is used. In FIG. 3, pre-processing unit 22 and BSS unit 24 areidentical to those in FIG. 2, so the description of these components isnot duplicated below. Post-processing unit 70 is similar to unit 26 ofFIG. 2, but essentially performs similar functions on both signal pathsrather than just one of the signal paths.

In FIG. 3, post-processing unit 70 includes two adaptive filters 74A and74B, and two delay circuits 75A and 75B. Adaptive filter 75B filters thesecond BSS signal, which is output from adder 32B. First BSS signal,which is output from adders 32A, is delayed via delay circuit 74A. Then,the adaptively filtered second BSS signal (output from adaptive filter75B) is subtracted from the delayed first BSS signal (output from delaycircuit 74A) via subtraction unit 77A. S₁″(z) represents the outputassociated with input signal X₁(z) following the pre-processing, BSStechniques and post-processing shown in FIG. 3. The S₁″(z) signal isalso used as feedback to control adaptive filter 75B, although thisfeedback is not illustrated for simplicity in the illustration.

The second signal path is processed similarly. In particular, adaptivefilter 75A filters the first BSS signal, which is output from adder 32A.Second BSS signal, which is output from adder 32B, is delayed via delaycircuit 74B. Then, the adaptively filtered first BSS signal (output fromadaptive filter 75A) is subtracted from the delayed second BSS signal(output from delay circuit 74B) via subtraction unit 77B. S₂″(z)represents the output associated with input signal X₂(z) following thepre-processing, BSS techniques and post-processing shown in FIG. 3. TheS₂″(z) signal is also used as feedback to control adaptive filter 75A,although this feedback is not illustrated for simplicity in theillustration.

FIG. 4 is a flow diagram illustrating a technique that may be performedaccording to an embodiment of this disclosure. As shown in FIG. 4,device 20 (see FIG. 2) receives first and second signals X₁(z) and X₂(z)(81). Signals X₁(z) and X₂(z) may comprise audio signals captured by twomicrophones, although this disclosure is not necessarily limited in thisrespect. The microphones, for example, may comprise omni-directionalmicrophones (or other types of microphones) and may be included indevice 20. The first signal X₁(z) may be highly correlated with thesecond signal X₂(z) due to close spatial proximity of the first andsecond microphones.

Pre-processing unit 22 performs pre-processing on second signal X₂(z) tode-correlate second signal X₂(z) from first signal X₁(z) (82). Thepre-processing may include applying adaptive filter 42 to the firstsignal X₁(z) and subtracting the adaptively filtered first signal(output from adaptive filter 42) from the second signal X₂(z). Inaddition, pre-processing the second signal X₂(z) may further comprisegenerating a calibration factor “c” based on a ratio of noise floors ofthe first signal relative X₁(z) to a difference X′₂(z) between thesecond signal X₂(z) and the adaptively filtered first signal (output ofadaptive filter 42). In addition, the pre-processing may furthercomprise applying the calibration factor “c” to the difference X′₂(z).In some cases, a delay may used such that pre-processing second signalX₂(z) further comprises delaying second signal X₂(z) prior tosubtracting the adaptively filtered first signal (output from adaptivefilter 42) from second signal X₂(z). The delay element not shown in FIG.2, but could be added if desired.

Next, BSS unit 24 performs a BSS technique on the first signal X₁(z) andthe pre-processed second signal X″₂(z) (83). The BSS technique mayinclude applying least mean square (LMS) adaptive filtering to the firstsignal X₁(z), and summing the LMS adaptive filtered first signal (outputof W₁₂(z)) with the pre-processed second signal X″₂(Z). In addition, theBSS technique may include applying LMS adaptive filtering to thepre-processed second signal X″₂(z), and summing the LMS adaptivefiltered and pre-processed second signal (output of W₂₁(z)) with thefirst signal.

Finally, post processing unit 26 performs post processing to furtherde-correlate the first and second BSS signals, which are output byadders 32A and 32B respectively (84). In the case of FIG. 2,post-processing the first BSS signal (output of adder 32A) comprisesapplying a second adaptive filter 52 to the second BSS signal (output ofadder 32B) and subtracting the adaptively filtered second BSS signal(output of adaptive filter 52) from the first BSS signal (output ofadder 32A). Post processing the first BSS signal (output of adder 32A)may further includes delaying the first BSS signal via delay circuit 54prior to subtracting the adaptively filtered second BSS signal (outputfrom adaptive filter 52) from the first BSS signal such that the delayedoutput of circuit 54 is used by subtraction unit 56.

The output of device 20 may comprise signal S′₁(z), which may comprise avery accurate representation of signal X₁(z) with noise reduced.Alternatively, if post processing unit 26 of FIG. 2 is replaced withpost processing unit 60 of FIG. 3, the output of device 70 may comprisesignals S″₁(z) and S″₂(z).

A number of embodiments have been described. However, variousmodifications could be made to the techniques described herein. Forexample, the pre- and/or post-processing techniques described herein maybe used with other BSS algorithms, which are not necessarily limited tothose illustrated in FIG. 1. In addition, although the techniques havebeen primarily described for use in speech enhancement, such techniquesmay find broad application for any environment where BSS techniques areused including other audio signal applications, noise reduction, andbio-medical signal processing including electrocardiogram (ECG) andelectroencephalogram (EEG) processing, and the like. For audio or speechprocessing the signals may be processed in real time for someapplications.

The techniques described herein may be implemented in hardware,software, firmware, or any combination thereof. If implemented insoftware, the techniques may be directed to a computer readable mediumcomprising program code, that when executed cause a device to performone or more of the techniques described herein. In that case, thecomputer readable medium may comprise random access memory (RAM) such assynchronous dynamic random access memory (SDRAM), read-only memory(ROM), non-volatile random access memory (NVRAM), electrically erasableprogrammable read-only memory (EEPROM), FLASH memory, and the like.

The program code may be stored on memory in the form of computerreadable instructions. In that case, a processor such as a DSP mayexecute instructions stored in memory in order to carry out one or moreof the BSS enhancement techniques described herein. In some cases, thetechniques may be executed by a DSP that invokes various hardwarecomponents to accelerate the process. In other cases, the unitsdescribed herein may be implemented as a microprocessor, one or moreapplication specific integrated circuits (ASICs), one or more fieldprogrammable gate arrays (FPGAs), one or more complex programmable logicdevice (CPLD), or some other hardware-software combination. The unitsdescribed herein may be integrated into common hardware, circuit orprocessor. Specifically, the pre-processing units and post-processingunits described in this disclosure could be implemented as one commonunit with the BSS units described herein.

These and other embodiments are within the scope of the followingclaims.

1. A method comprising: receiving a first signal associated with a firstsensor and a second signal associated with a second sensor;pre-processing the second signal to de-correlate the second signal fromthe first signal; applying a blind source separation (BSS) technique tothe first signal to generate a first BSS signal; and applying the BSStechnique to the pre-processed second signal to generate a second BSSsignal.
 2. The method of claim 1, wherein pre-processing the secondsignal comprises applying an adaptive filter to the first signal andsubtracting the adaptively filtered first signal from the second signal.3. The method of claim 2, wherein pre-processing the second signalfurther comprises delaying the second signal prior to subtracting theadaptively filtered first signal from the second signal.
 4. The methodof claim 3, wherein pre-processing the second signal further comprisesgenerating a calibration factor based on a ratio of noise floors of thefirst signal relative to a difference between the second signal and theadaptively filtered first signal.
 5. The method of claim 4, whereinpre-processing further comprises applying the calibration factor to thedifference.
 6. The method of claim 1, further comprising:post-processing the first BSS signal to further de-correlate the firstBSS signal from the second BSS signal; and outputting the post-processedfirst BSS signal.
 7. The method of claim 6, wherein: pre-processing thesecond signal comprises applying a first adaptive filter to the firstsignal and subtracting the adaptively filtered first signal from thesecond signal; and post-processing the first BSS signal includesapplying a second adaptive filter to the second BSS signal andsubtracting the adaptively filtered second BSS signal from the first BSSsignal.
 8. The method of claim 7, wherein post-processing the first BSSsignal further includes delaying the first BSS signal prior tosubtracting the adaptively filtered second BSS signal from the first BSSsignal.
 9. The method of claim 8, wherein pre-processing the secondsignal further comprises: generating a calibration factor based on aratio of noise floors of the first signal relative to a differencebetween the second signal and the adaptively filtered first signal, andapplying the calibration factor to the difference.
 10. The method ofclaim 1, further comprising: post-processing the first and second BSSsignals to further de-correlate the first BSS signal from the second BSSsignal.
 11. The method of claim 1, wherein applying the BSS techniqueincludes: applying least mean square (LMS) adaptive filtering to thefirst signal; summing the LMS adaptive filtered first signal with thepre-processed second signal; applying LMS adaptive filtering to thepre-processed second signal; and summing the LMS adaptive filtered andpre-processed second signal with the first signal.
 12. The method ofclaim 1, wherein the first signal comprises a first audio signalassociated with a first microphone and the second signal comprises asecond audio signal associated with a second microphone.
 13. The methodof claim 12, further comprising capturing the first audio signal withthe first microphone and capturing the second audio signal with thesecond microphone.
 14. The method of claim 13, further comprisingconverting the captured audio signals from analog signals into digitalsignals.
 15. The method of claim 13, wherein the first audio signal ishighly correlated with the second audio signal due to close spatialproximity of the first and second microphones.
 16. The method of claim15, wherein the method is executed in a wireless communication device.17. A device comprising: a first sensor that generates a first signaland a second sensor that generates a second signal; a pre-processingunit that pre-processes the second signal to de-correlate the secondsignal from the first signal; and a blind source separation (BSS) unitthat applies a BSS technique to the first signal and the pre-processedsecond signal to generate first and second BSS signals respectively. 18.The device of claim 17, wherein the pre-processing unit applies anadaptive filter to the first signal and subtracts the adaptivelyfiltered first signal from the second signal.
 19. The device of claim18, wherein the pre-processing unit generates a calibration factor basedon a ratio of noise floors of the first signal relative to a differencebetween the second signal and the adaptively filtered first signal. 20.The device of claim 19, wherein the pre-processing unit applies thecalibration factor to the difference.
 21. The device of claim 17,further comprising a post-processing unit that post-processes the firstBSS signal to further de-correlate the first BSS signal from the secondBSS signal, and outputs the post-processed first BSS signal.
 22. Thedevice of claim 21, wherein: the pre-processing unit applies a firstadaptive filter to the first signal and subtracts the adaptivelyfiltered first signal from the second signal; and post-processing unitapplies a second adaptive filter to the second BSS signal and subtractsthe adaptively filtered second BSS signal from the first BSS signal. 23.The device of claim 22, wherein the post-processing unit delays thefirst BSS signal prior to subtracting the adaptively filtered second BSSsignal from the first BSS signal.
 24. The device of claim 23, whereinthe pre-processing unit: generates a calibration factor based on a ratioof noise floors of the first signal relative to a difference between thesecond signal and the adaptively filtered first signal, and applies thecalibration factor to the difference.
 25. The device of claim 21,wherein the post-processing unit post-processes the first and second BSSsignals to further de-correlate the first BSS signal from the second BSSsignal.
 26. The device of claim 17, wherein the pre-processing unitdelays the second signal prior to subtracting the adaptively filteredfirst signal from the second signal.
 27. The device of claim 17, whereinthe BSS unit: applies least mean square (LMS) adaptive filtering thefirst signal; sums the LMS adaptive filtered first signal with thepre-processed second signal; applies LMS adaptive filtering to thepre-processed second signal; and sums the LMS adaptive filtered andpre-processed second signal with the first signal.
 28. The device ofclaim 17, wherein the first sensor comprises a first microphone and thesecond sensor comprises a second microphone, and wherein the firstsignal comprises a first audio signal associated with the firstmicrophone and the second signal comprises a second audio signalassociated with the second microphone.
 29. The device of claim 28,wherein the first audio signal is highly correlated with the secondaudio signal due to close spatial proximity of the first and secondmicrophones.
 30. The device of claim 17, wherein the device comprises awireless communication device.
 31. A computer-readable medium comprisinginstructions that upon execution: receive a first signal and a secondsignal; pre-process the second signal to de-correlate the second signalfrom the first signal; and apply a blind source separation (BSS)technique to the first signal and the pre-processed second signal togenerate first and second BSS signals respectively.
 32. Thecomputer-readable medium of claim 31, wherein the pre-process of thesecond signal includes: applying an adaptive filter to the first signal;subtracting the adaptively filtered first signal from the second signal;generating a calibration factor based on a ratio of noise floors of thefirst signal relative to a difference between the second signal and theadaptively filtered first signal; and applying the calibration factor tothe difference.
 33. The computer-readable medium of claim 31, whereinthe instructions: post-process the first BSS signal to furtherde-correlate the first BSS signal from the second BSS signal.
 34. Thecomputer-readable medium of claim 33, wherein the pre-process of thesecond signal includes: applying an adaptive filter to the first signal;applying delay to the second signal; subtracting the adaptively filteredfirst signal from the delayed second signal; generating a calibrationfactor based on a ratio of noise floors of the first signal relative toa difference between the second signal and the adaptively filtered firstsignal; and applying the calibration factor to the difference, andwherein the post-process of the first BSS signal includes: delaying thefirst BSS signal; applying a second adaptive filter to the second BSSsignal; and subtracting the adaptively filtered second BSS signal fromthe delayed first BSS signal.
 35. The computer-readable medium of claim31, wherein the first signal comprises a first audio signal associatedwith a first microphone and the second signal comprises a second audiosignal associated with a second microphone.
 36. An apparatus comprising:means for generating a first signal; means for generating a secondsignal; means for pre-processing the second signal to de-correlate thesecond signal from the first signal; and means for applying a blindsource separation (BSS) technique to the first signal and thepre-processed second signal to generate first and second BSS signalsrespectively.
 37. The apparatus of claim 36, further comprising meansfor post-processing the first BSS signal to further de-correlate thefirst BSS signal from the second BSS signal.